{"id":"W2783403690","doi":"10.1177/0960336017751466","title":"Using blood near infrared spectra from steers to classify fat and meat samples with low or high levels of vaccenic acid","year":2018,"lang":"en","type":"article","venue":"NIR news","topic":"Meat and Animal Product Quality","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Natural Sciences and Engineering Research Council of Canada; O and T Farms","keywords":"Subcutaneous fat; Longissimus Thoracis; Partial least squares regression; Vaccenic acid; Chemistry; Near-infrared spectroscopy; Food science; Animal science; Chromatography; Fatty acid; Biology; Biochemistry; Adipose tissue; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009576452,0.0001437902,0.0002491696,0.000008228803,0.0001546741,0.00007784241,0.0001722559,0.00007277488,0.001102204],"category_scores_gemma":[0.0000639782,0.00004798061,0.00003388559,0.0002515087,0.0001309774,0.0001223567,0.00007110089,0.00007158487,0.00001241286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009116574,"about_ca_system_score_gemma":0.00001497942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00217022,"about_ca_topic_score_gemma":0.004151613,"domain_scores_codex":[0.9989653,0.00006814123,0.0002013844,0.0003537046,0.0001863121,0.0002251275],"domain_scores_gemma":[0.9995584,0.0000773347,0.00008370462,0.00009070721,0.0000677136,0.0001220721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002266643,0.00004866367,0.01520585,0.000008466138,0.00002710151,0.000002346374,0.0003012856,5.684508e-7,0.9738558,0.0000900803,0.0002168001,0.01001636],"study_design_scores_gemma":[0.0004389723,0.00133924,0.4798502,0.00007434313,0.00005323877,0.000004851923,0.0006471763,0.00001694575,0.5136787,0.0007793782,0.00282799,0.0002890395],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986967,0.00005864009,0.00002070795,0.000520901,0.00005918873,0.0001849358,0.0001395048,0.00003021772,0.000289237],"genre_scores_gemma":[0.9932538,0.000007438732,0.005718938,0.0002807365,0.0005231051,0.00000152053,0.0000110742,0.000001567119,0.0002018147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4646443,"threshold_uncertainty_score":0.9998109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1066971241876632,"score_gpt":0.2799886402759493,"score_spread":0.1732915160882861,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}